Method and system for non-invasive optical blood glucose detection utilizing spectral data analysis

ABSTRACT

Systems and methods are disclosed for non-invasively measuring blood glucose levels in a biological sample based on spectral data. This includes at least one light source configured to strike a target area of a sample, at least one light detector, which includes a preamplifier having a feedback resistor, positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, and a processor configured to receive the output signal from the at least one light detector and based on the received output signal, calculate the attenuance attributable to blood in a sample present in the target area and eliminate effect of uncertainty caused by temperature dependent detector response of the at least one light detector, and based on the calculated attenuance, determine a blood glucose level associated with a sample.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional of prior U.S. patent application Ser.No. 12/425,535, filed Apr. 17, 2009, which is hereby incorporated hereinby reference in its entirety, and also claims priority to U.S.Provisional Patent Application Ser. No. 61/055,303, filed on May 22,2008, the disclosure of which is incorporated herein by reference, andalso claims priority to U.S. Provisional Patent Application Ser. No.61/089,152, filed on Aug. 15, 2008, the disclosure of which isincorporated herein by reference.

BACKGROUND OF THE INVENTION

Diabetes is a chronic disease that, when not controlled, over time leadsto serious damage to many of the body's systems, including the nerves,blood vessels, eyes, kidneys and heart. The National Institute ofDiabetes and Digestive and Kidney Diseases (NIDDK) estimates that 23.6million people, or 7.8 percent of the population in the United States,had diabetes in 2007. Globally, the World Health Organization (WHO)estimates that more than 180 million people have diabetes, a number theyexpect to increase to 366 million by 2030, with 30.3 million in theUnited States. According to the WHO, an estimated 1.1 million peopledied from diabetes in 2005. They project that diabetes deaths willincrease by more than 50% between 2006 and 2015 overall and by more than80% in upper-middle income countries.

The economic burden from diabetes for individuals and society as a wholeis substantial. According to the American Diabetes Association, thetotal annual economic cost of diabetes was estimated to be $174 billionin the United States in 2007. This is an increase of $42 billion since2002. This 32% increase means the dollar amount has risen over $8billion more each year.

A vital element of diabetes management is the self-monitoring of bloodglucose (SMBG) concentration by diabetics in the home environment. Bytesting blood glucose levels often, diabetics can better managemedication, diet, and exercise to maintain control and prevent thelong-term negative health outcomes. In fact, the Diabetes Control andComplications Trial (DCCT), which followed 1,441 diabetics for severalyears, showed that those following an intensive-control program withmultiple blood sugar tests each day, as compared with thestandard-treatment group, had only one-fourth as many people developdiabetic eye disease, half as many develop kidney disease, one-third asmany develop nerve disease, and far fewer people who already had earlyforms of these three complications got worse.

However, current monitoring techniques discourage regular use due to theinconvenient and painful nature of drawing blood through the skin priorto analysis, which causes many diabetics to not be as diligent as theyshould be for good blood glucose control. As a result, non-invasivemeasurement of glucose concentration is a desirable and beneficialdevelopment for the management of diabetes. A non-invasive monitor willmake testing multiple times each day pain-free and more palatable forchildren with diabetes. According to a study published in 2005 (J.Wagner, C. Malchoff, and G. Abbott, Diabetes Technology & Therapeutics,7(4) 2005, 612-619), people with diabetes would perform SMBG morefrequently and have improved quality of life with a non-invasive bloodglucose monitoring device.

There exist a number of non-invasive approaches for blood glucosedetermination. One technique of non-invasive blood chemical detectioninvolves collecting and analyzing light spectra data.

Extracting information about blood characteristics, such as glucoseconcentration from spectral or other data obtained from spectroscopy, isa complex problem due to the presence of components (e.g., skin, fat,muscle, bone, interstitial fluid) other than blood in the area that isbeing sensed. Such other components can influence these signals in sucha way as to alter the reading. In particular, the resulting signal maybe much larger in magnitude than the portion of the signal thatcorresponds to blood, and therefore limits the ability to accuratelyextract blood characteristics information.

The present invention is directed to overcoming one or more of theproblems set forth above.

SUMMARY OF INVENTION

In an aspect of the present invention, a system for detecting glucose ina biological sample is disclosed. This system includes at least onelight source configured to strike a target area of a sample, at leastone light detector, which includes a preamplifier having a feedbackresistor, positioned to receive light from the at least one light sourceand to generate an output signal, having a time dependent current, whichis indicative of the power of light detected, and a processor configuredto receive the output signal from the at least one light detector andbased on the received output signal, calculate the attenuanceattributable to blood in a sample present in the target area andeliminate effect of uncertainty caused by temperature dependent detectorresponse of the at least one light detector, and based on the calculatedattenuance, determine a blood glucose level associated with a samplepresent in the target area.

In yet another aspect of the present invention, a method for detectingglucose in a biological sample is disclosed. The method includesutilizing at least one light source configured to strike a target areaof a sample utilizing at least one light detector, which includes apreamplifier having a feedback resistor, positioned to receive lightfrom the at least one light source and generating an output signal,having a time dependent current, which is indicative of the power oflight detected, and utilizing a processor that receives the outputsignal from the at least one light detector and based on the receivedoutput signal, calculating the attenuance attributable to blood in asample present in the target area and eliminating effect of uncertaintycaused by temperature dependent detector response of the at least onelight detector, and based on the calculated attenuance, determining ablood glucose level associated with a sample present in the target area.

These are merely some of the innumerable aspects of the presentinvention and should not be deemed an all-inclusive listing of theinnumerable aspects associated with the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the present invention, reference may bemade to accompanying drawings, in which:

FIG. 1 illustrates a plot of a pulse wave corresponding to lightabsorption of arterial blood, according to exemplary embodiments;

FIG. 2 illustrates an exemplary system for obtaining spectral data;

FIG. 3 illustrates a plot of A (t), calculated according to Equation (9)using data in FIG. 1; and

FIG. 4 is a basic illustrative schematic of a preamplifier circuit thatconverts photocurrent into voltage prior to digitization.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, numerous exemplary specificdetails are set forth in order to provide a thorough understanding ofthe invention. However, it will be understood by those skilled in theart that the present invention may be practiced without these specificdetails, or with various modifications of the details. In otherinstances, well known methods, procedures, and components have not beendescribed in detail so as not to obscure the present invention.

Optical spectroscopy can be used to determine the amount of lightabsorbed and scattered, i.e., attenuated, by a biological sample such asa human finger. By measuring the amount of light absorbed by the sample,it is possible to determine glucose, cholesterol, and hemoglobin levelsof a subject non-invasively. Fingertip measurements are usuallypreferred because of the large concentration of capillaries in thefingertip and because of the conversion of arterial blood into venousblood that occurs in the fingertip. However, the techniques of thepresent invention are not limited to use with a fingertip. For example,the biological sample could be a human earlobe.

When light is transmitted through a biological sample, such as a humanfinger, the light is attenuated by various components of the fingerincluding skin, muscle, bone, fat, interstitial fluid and blood. It hasbeen observed, however, that light attenuation by a human fingerexhibits a small cyclic pattern that corresponds to a heartbeat. It isbelieved that this cyclic pattern will be present in measurements ofmany other human body parts, the earlobe being one of many examples.

FIG. 1 depicts a plot 102 of a detector photocurrent, I_(D)(t), thatcorresponds to the power of light received by a detector after the lighthas passed through a subject's finger. As can be seen, the detectorphotocurrent exhibits a cyclic pattern. This cyclic pattern is due tothe subject's heartbeat, which cyclically increases and decreases thequantity of blood in the subject's capillaries (or other structures).Although the magnitude of the cyclic pattern is small in comparison tothe total photocurrent generated by the detector, considerableinformation can be extracted from the cyclic pattern of the plot 102.For example, assuming that the person's heart rate is sixty beats perminute, the time between the start of any pulse beat and the end of thatpulse beat is one second. During this one-second period, thephotocurrent will have a maximum or peak reading 104 and minimum orvalley reading 106. The peak reading 104 of the plot corresponds to whenthere is a minimum amount of blood in the capillaries, and the valleyreading 106 corresponds to when there is a maximum amount of blood inthe capillaries. By using information provided by the peak and valley ofthe cyclic plot, the optical absorption and scattering by major fingerconstituents that are not in the capillaries, such as skin, fat, bones,muscle and interstitial fluids, are excluded. These major constituentsthat are not in the capillaries are excluded because they are not likelyto change during the time interval of one heartbeat. In other words, thelight that is absorbed and scattered, i.e., attenuated, by the blood canbe detected based on the peaks and valleys of the plot 102.

Assuming that the peak of the cyclic photocurrent generated by thelight-sensing device is I_(P), the adjacent valley of the cyclicphotocurrent is I_(V), and the photocurrent generated by thelight-sensing device without a human finger is I₀, the transmittancescorresponding to the peak and valley photocurrents can be defined as:

$\begin{matrix}{{T_{V} = \frac{I_{V}}{I_{0}}}{and}} & (1) \\{T_{P} = \frac{I_{P}}{I_{0}}} & (2)\end{matrix}$

The corresponding peak and valley absorbance are:

A _(V)=log(T _(V))  (3)

and

A _(P)=log(T _(P))  (4)

The difference between A_(V) and A_(P) represents the light absorptionand scattering by the blood in the finger, excluding non-bloodconstituents:

$\begin{matrix}{{\Delta \; A} = {{A_{V} - A_{P}} = {\log \left( \frac{I_{P}}{I_{V}} \right)}}} & (5)\end{matrix}$

As can be seen in the algorithm shown in Equation (5), AA does notdepend on I₀. Thus, calculating AA does not require a determination ofthe current generated by the light-sensing device without a sample.Monitoring the photocurrent corresponding to light power transmittedthrough a sample is sufficient to calculate ΔA.

FIG. 2 depicts a simplified block diagram of an exemplary apparatus foruse in an exemplary embodiment. Optical measurement system, which isgenerally indicated by numeral 200, uses the “pulsatile” concept fordetermining an amount of light absorbed and scattered solely by theblood in a sample (a human finger in this exemplary embodiment). A powersource 201, such as a battery, provides power to a light source 202 thatgenerates a plurality of light beams 204, 206, 208, 210 that aredirected toward the top of the finger of a subject. In an exemplaryembodiment, each of the light beams 204, 206, 208, 210 have the samewavelength or a different wavelength range, typically within 800 nm to1600 nm. Although the optical measurement system 200 is described hereinas generating four (4) light beams, it is contemplated that the lightsource 202 can be altered to generate fewer light beams or additionallight beams in other embodiments.

A first aperture 212 ensures that the light beams 204, 206, 208, 210strike a target area of the finger. A second aperture 214 ensures thatthe portion of the light beams that are transmitted through the fingerstrike a lens 216. Light beams 204, 206, 208, 210 are attenuated by thefinger and components of the optical measurement system 200, and, thus,attenuated light beams 218, 220, 222, 224 are emitted from the finger.The attenuated light beams 218, 220, 222, 224 strike the lens 216, andthe lens 216 collects the attenuated light beams 218, 220, 222, 224 sothat they impinge more efficiently on a detector block 226.

The detector block 226 is positioned directly under the lens 216 andcomprises a plurality of light-sensing devices (LSD) 228, 230, 232, 234such as an array of photodiodes. According to one aspect of the opticalmeasurement system 200, each of the light-sensing devices 228, 230, 232,234 detects a specific wavelength of light as defined by correspondinginterference filters (IF) 236, 238, 240, 242, respectively. Theinterference filter transmits one or more spectral bands or lines oflight, and blocks others.

Each of the light-sensing devices 228, 230, 232, 234 generates acorresponding photocurrent signal that is proportional to the power ofthe light received by the particular light sensing device. Thephotocurrent signal generated by the photodiode can be converted toanother form of signal, such as an analog voltage signal or a digitalsignal. A processor 243 is coupled to the detector block 226 and isconfigured to calculate the change of photocurrent signals 244, 246,248, 250.

According to one aspect, the processor 243 executes an algorithm such asshown in the Equation (5) to calculate the change in the lightabsorption (AA) solely caused by the blood in the finger. Thereafter,this quantitative calculation of light absorption of the blood can beused to determine a characteristic of the blood. For example, bycomparing the calculated light absorption value to predetermined valuescorresponding to different glucose levels stored in a memory (notshown), a blood-glucose level of the subject can be determined.

A difficulty associated with the finger based pulsatile detectionmethodology is low signal-to-noise (S/N) ratio, because the amplitude ofcyclic pattern (i.e., the difference between peak and valley) istypically 1%-2% of the total photocurrent generated by the light powertransmitted through the finger. To obtain a S/N ratio of 100:1 in thedetermination of AA, the baseline noise of the device being used tomeasure the light absorption by the finger should not be larger than3.0×10⁻⁵ in absorbance (peak to peak), within a 10 Hz bandwidth.

However, a 3.0×10⁻⁵ absorbance (peak to peak) baseline noise levelwithin a 10 Hz bandwidth is difficult to obtain with the low light powerlevels that are used by some battery-powered hand held non-invasiveblood chemical measurement devices. One solution involves dataaveraging. To increase the S/N ratio, the averaged value of ΔA, asdefined by the Equation below, is used in further calculation to extractblood glucose concentration:

$\begin{matrix}{\overset{\_}{\Delta \; A} = {\sum\limits_{j = 1}^{M}{\Delta \; A_{j}}}} & (6)\end{matrix}$

In Equation (6), M is the number of heartbeats during the time intervalof the pulsatile measurement. However, this approach requires long dataacquisition time, due to the fact that the rate of heartbeat is in theorder of one per second. For example, 25 seconds would be needed forincreasing the S/N ratio by a factor of five, and 100 seconds would beneeded for increasing the S/N ratio by a factor of ten. In comparison,current commercial blood drawing glucose meters can determine bloodglucose level within 5 seconds. Furthermore, long detection time willsignificantly increase measurement errors due to finger movement, lightpower drift, device temperature change, etc. Thus, there is a need fornew techniques to measure blood glucose levels quickly and accurately.

Improving S/N Ratio by Standard Deviation

The time dependent detector photocurrent output, I_(D)(t), shown in FIG.1 can be expressed as the sum of a small time dependent cyclicphotocurrent ΔI(t), corresponding to the heartbeat, a noise currentn(t), and a constant baseline photocurrent I_(B):

I _(D)(t)=I _(B) +ΔI(t)+n(t)  (7)

The above Equation can be re-arranged as:

$\begin{matrix}{\frac{I_{D}(t)}{I_{B}} = {1 + \frac{{\Delta \; {I(t)}} + {n(t)}}{I_{B}}}} & (8)\end{matrix}$

Applying common logarithm to both side of the Equation (8), one obtains:

$\begin{matrix}{{A(t)} = {{\log \left\lbrack \frac{I_{D}(t)}{I_{B}} \right\rbrack} = {\log \left( {1 + \frac{{\Delta \; {I(t)}} + {n(t)}}{I_{B}}} \right)}}} & (9)\end{matrix}$

FIG. 3, which is generally indicated by numeral 300, shows a typicalA(t) plot 302, calculated according to Equation (9) using data inFIG. 1. For a pulse function A(t) shown in FIG. 3, the following keyrelationship exists during the time interval of one heartbeat:

σ[A(t)]=kΔA  (10)

in which σ[A(t)] is the Standard Deviation of A(t), and k is aproportional constant.

Considering the fact that I_(B) is a constant and σ²(log I_(B))=0, oneobtains:

σ[A(t)]=σ[log I _(D)(t)]  (12)

Therefore, the peak-to-valley height of the A(t) plot during the timeinterval of one heartbeat can be obtained directly from the standarddeviation of the logarithm of I_(D)(t):

$\begin{matrix}{{\Delta \; A} = {\frac{\sigma \left\lbrack {A(t)} \right\rbrack}{k} = \frac{\sigma \left\lbrack {\log \; {I_{D}(t)}} \right\rbrack}{k}}} & (13)\end{matrix}$

A major advantage of Equation (13) is that high S/N ratio can beachieved within short data acquisition time (approximately one second),as explained below.

In a finger based pulsatile measurement depicted by FIG. 2, the value ofthe sum, ΔI(t)+n(t) is typically less than 2% of the large constantbaseline photocurrent I_(B). Therefore, Equation (9) can be approximatedas:

$\begin{matrix}{{A(t)} = {{\log \left\lbrack \frac{I_{D}(t)}{I_{B}} \right\rbrack} \approx {\frac{1}{\ln \; 10}\frac{{\Delta \; {I(t)}} + {n(t)}}{I_{B}}}}} & (14)\end{matrix}$

Similarly, the standard deviation of A(t) can be approximated as:

$\begin{matrix}{{\sigma \left\lbrack {A(t)} \right\rbrack} \approx {\frac{1}{\ln \; 10}\frac{\sqrt{{\sigma^{2}\left\lbrack {\Delta \; {I(t)}} \right\rbrack} + {\sigma^{2}\left\lbrack {n(t)} \right\rbrack}}}{I_{B}}}} & (15)\end{matrix}$

Equation (15) demonstrates great noise reduction power of Equation (13).For example, for a relatively high baseline noise with the ratio

${\rho = {\frac{\sigma \left\lbrack {n(t)} \right\rbrack}{\sigma \left\lbrack {\log \; \Delta \; {I(t)}} \right\rbrack} = {0.1\mspace{14mu} \left( {{or}\mspace{14mu} 10\%} \right)}}},$

the contribution to σ[A(t)] from the baseline noise n(t) is estimated tobe less than 0.005 (or 0.5%), corresponding to an increase in S/N ratioby a factor of 20 without increasing detection time. As such, dramaticnoise reduction can be obtained without increasing the data acquisitiontime, and a finger based pulsatile measurement can be completed withinthe time interval of one heartbeat (which is approximately one second),and the requirement for the S/N ratio of 100 to 1 in determination of AAcan be satisfied using an optical system with a baseline noise of about6.0×10⁻⁴ absorbance (peak to peak) within a 10 Hz bandwidth. It shouldbe pointed out that when the baseline noise of an optical system isdominated by shot noise due to low light illumination power, a noisereduction by a factor of 20 equals an increasing in light illuminationpower by a factor of 20²=400.

This ability of obtaining higher S/N ratio within the very short dataacquisition time, e.g., less than one second, will significantly reducedetection error caused by factors such as finger movement, temperaturechange, and light power drift during the measurement, and thereforedramatically improve the accuracy and reproducibility of the pulsatiledetection methodology.

Furthermore, the value of k does not change with wavelength, becausetransmitted lights at all wavelengths have identical pulse shape due tothe heartbeat. As a result, the constant k will be cancelled in datanormalization discussed in next section, and σ[log I_(D)(t)] will beused in further regression analysis to establish correlation between theoptical measurement and blood glucose level. This will greatly simplifythe data analysis process since σ[log I_(D)(t)] involves only twostandard math functions available in most popular spreadsheet programssuch as Microsoft EXCEL®. EXCEL® is a federally registered trademark ofMicrosoft Corporation, having a place of business at One Microsoft Way,Redmond, Wash. 98052-6399.

Normalization

At each wavelength λ_(i), the absorption ΔA(λ_(i)) is linked to theincrease of amount of blood (ΔB) in the optical sensing area of thefingertip due to the heartbeat by the following Equation:

ΔA(λ_(i))=ε(C,λ _(i) ,T)ΔB  (16)

in which ε(C,λ_(i),T) is the absorption/scattering coefficient of bloodat wavelength λ_(i), finger temperature T, and blood glucoseconcentration C. It is well understood that the variable ΔB differs fromperson to person, and may even change from day to day for the sameperson.

The uncertainty from the variable ΔB can be cancelled by introducing thenormalization factor

(C,T) at each wavelength λ₄, as defined by the Equation below:

$\begin{matrix}{{{Q_{i}\left( {C,T} \right)} = {\frac{\Delta \; {A\left( \lambda_{i} \right)}}{\sum\limits_{i = 1}^{N}{\Delta \; {A\left( \lambda_{i} \right)}}} = \frac{ɛ\left( {C,\lambda_{i},T} \right)}{\sum\limits_{i = 1}^{N}{ɛ\left( {C,\lambda_{i},T} \right)}}}},} & (17)\end{matrix}$

in which N is total number of wavelength employed. Preferably, Ntypically ranges from twenty to thirty.

Based on Equations (13) and (17),

_(i)(C,T) is linked to the detector photocurrent at each wavelengthλ_(i), I_(D)(λ_(i),t), by the following Equation:

$\begin{matrix}{{{Q_{i}\left( {C,T} \right)} = {\frac{\Delta \; {A\left( \lambda_{i} \right)}}{\sum\limits_{i = 1}^{N}{\Delta \; {A\left( \lambda_{i} \right)}}} = {\frac{{\sigma \left\lbrack {\log \; {I_{D}\left( {\lambda_{i},t} \right)}} \right\rbrack}/k}{\sum\limits_{i = 1}^{N}{{\sigma \left\lbrack {\log \; {I_{D}\left( {\lambda_{i},t} \right)}} \right\rbrack}/k}} = \frac{\sigma \left\lbrack {\log \; {I_{D}\left( {\lambda_{i},t} \right)}} \right\rbrack}{\sum\limits_{i = 1}^{N}{\sigma \left\lbrack {\log \; {I_{D}\left( {\lambda_{i},t} \right)}} \right\rbrack}}}}},} & (18)\end{matrix}$

As shown by Equation (18), the constant k is cancelled and σ[logI_(D)(t)] will be used in further regression analysis to establishcorrelation between the optical measurement and blood glucose level.This is possible because data are taken simultaneously from alldetection channels.

A correlation between optical measurement and blood glucoseconcentration can be established according to the following Equation:

$\begin{matrix}{C_{optical} = {\sum\limits_{i = 1}^{N}{{a_{i}(T)}{Q_{i}\left( {C,T} \right)}}}} & (19)\end{matrix}$

in which C_(optical) is the blood glucose concentration predicted by theoptical measurement,

_(i)(C,T) is defined by Equations (17) and (18), and a_(i)(T) is thetemperature dependent regression coefficient corresponding to wavelengthλ_(i). The values of a_(i)(T) can be extracted using proper statisticsmethods such as Partial Least Squares (PLS) regression.

Equation (19) represents ideal cases when large number of calibrationscan be made at different finger temperatures. In reality, frequentlyonly a limited number of calibrations can be made (e.g., 15 to 20), andeach may be taken at a different finger temperature. Under thiscondition, the finger temperature can be treated as an independentvariable, and the above Equation can be approximated as:

$\begin{matrix}{C_{optical} = {{\sum\limits_{i = 1}^{N}{b_{i}{Q_{i}\left( {C,T} \right)}}} + {\eta \; T}}} & (20)\end{matrix}$

in which b, is the temperature independent regression coefficientcorresponding to wavelength λ_(i), and η is the regression coefficientfor the finger temperature. The values of b_(i) and that of η can beextracted using proper statistics methods such as Partial Least Squares(PLS) regression.

Ratio Methodology

Alternatively, the uncertainty from the variable ΔB can be cancelled byintroducing a ratio factor Y_(ij) at wavelength λ_(i):

$\begin{matrix}{{{Y_{ij}\left( {C,T} \right)} = {\frac{\Delta \; {A\left( \lambda_{i} \right)}}{\Delta \; {A\left( \lambda_{j} \right)}} = {\frac{ɛ\left( {C,\lambda_{i},T} \right)}{ɛ\left( {C,\lambda_{j},T} \right)} = \frac{\sigma \left\lbrack {\log \; {I_{D}\left( {\lambda_{i},t} \right)}} \right\rbrack}{\sigma \left\lbrack {\log \; {I_{D}\left( {\lambda_{j},t} \right)}} \right\rbrack}}}},} & (21)\end{matrix}$

in which j can be any number from 1 to N, assuming that the devicecollects signal at all N wavelengths.

Similar to the normalization algorithm discussed before, a correlationbetween optical measurement and blood glucose level can be establishedaccording to the following Equation:

$\begin{matrix}{C_{optical} = {\sum\limits_{i \neq j}^{N}{{f_{i}(T)}{Y_{ij}\left( {C,T} \right)}}}} & (22)\end{matrix}$

in which C_(optical) is the blood glucose concentration predicted by theoptical measurement, Y_(ij)(C,T) is defined by Equation (21), andf_(i)(T) is the temperature dependent regression coefficientcorresponding to wavelength λ_(i). The value of f_(i)(T) can be obtainedusing statistics methods such as Partial Least Squares (PLS) regression.

Equation (22) represents ideal cases when large number of calibrationcan be made at different finger temperatures. In reality, frequentlyonly limited number of calibration can be made (e.g., 15 to 20), andeach may be taken at a different finger temperature. Under thiscondition, the finger temperature can be treated as an independentvariable, and the above Equation can be approximated as:

$\begin{matrix}{C_{optical} = {{\sum\limits_{i \neq j}^{N}{h_{i}{Y_{ij}\left( {C,T} \right)}}} + {\beta \; T}}} & (23)\end{matrix}$

in which h_(i) is the temperature independent regression coefficientcorresponding to wavelength λ_(i), and β is the regression coefficientfor the finger temperature. The values of h_(i) and that of β can beextracted using proper statistics methods such as Partial Least Squares(PLS) regression.

Elimination of the Effect of Temperature Dependent Device Response

It is well understood that the detector sensitivity of a siliconphotodiode detector is a function of wavelength and temperature. For thedevice configuration shown in FIG. 2, which is generally indicated bynumeral 200, the light power received by ith silicon diode detector,corresponding to wavelength λ_(i), is converted into a photocurrentaccording to the following Equation:

I _(D)(λ_(i) ,t)=P(λ_(i) ,t)S ₀(λ_(i))[1+γ(λ_(i))(T _(Di)(t)−25°C.)]  (24)

In the above Equation (24), P(λ_(i),t) is the light power received bythe detector, S₀(λ_(i)) is the photosensitivity of the detector atwavelength λ_(i) and 25° C., γ(λ_(i)) is the temperature coefficient ofthe photosensitivity at wavelength λ_(i), and T_(Di)(t) is thetemperature of ith photodiode detector. The temperature coefficientγ(λ_(i)) varies with the wavelength. For example, for Hamamatsu S1337series photodiode detectors, γ(λ_(i)) ranges from near zero at 900 nm toover 1.0%/° C. at 1100 nm. This imposes a potential problem for thedevice configuration show in FIG. 2, because it is very difficult tokeep temperature of each individual diode detector constant in ahandheld device used by a person with diabetes under a normalhousehold/office environment.

This uncertainty due to the detector temperature T_(Di)(t) can beeliminated using the algorithm shown by Equations (12) and (13).Applying common logarithm on both sides of the Equation (24), oneobtains:

log I _(D)(λ_(i) ,t)=log P(λ_(i) ,t)+log S ₀(λ_(i))+log [1+γ(λ_(i))(T_(Di)(t)−25° C.)]  (25)

Considering the fact that S₀(λ_(i)) is a constant and that detectortemperature T_(Di)(t) remains almost constant during the very short dataacquisition time interval of approximately one second, one obtains:

σ[log I _(D)(λ_(i) ,t)]=σ[log P(λ_(i) ,t)]  (26)

As such, the uncertainty caused by detector temperature T_(Di)(t) iseliminated by the use of this standard deviation methodology.

Voltage Detection Mode

In the device configuration shown in FIG. 2, the photocurrent of ithphotodiode detector I_(D)(λ_(i),t) is typically converted into a voltageusing a preamplifier before digitization. FIG. 4 shows the schematiccircuit diagram of a typical preamplifier, which is generally indicatedby numeral 400.

The output voltage 412 of ith preamplifier 400, in coupling with ithphotodiode detector 408, can be expressed as:

V _(i)(t)=R _(i) I _(D)(λ_(i) ,t)R _(0i)[1+χ_(i)(T _(Ri)(t)−25+C)]I_(D)(λ_(i) ,t)  (27)

In the above Equation (27), R_(0i) is the resistance value of feedbackresistor 402 for ith preamplifier at 25° C., χ_(i) is the temperaturecoefficient of the resistor, and T_(Ri)(t) is the temperature of theresistor. Applying common logarithm to both side of the Equation (27),one obtains:

log V _(i)(t)=log R _(i0)+log [1+λ_(i)(T _(Ri)(t)−25° C.)]+log I_(D)(λ_(i) ,t)  (28)

Considering the fact that R_(0i) is a constant and that the resistortemperature T R_(i)(t) does not change during the very short dataacquisition time interval of approximately one second, one obtains:

σ[log V _(i)(t)]=σ[log I _(D)(λ_(i) ,t)]  (29)

Substituting Equation (26) into Equation (29), one obtains:

σ[log V _(i)(t)]=σ[log P(λ_(i) ,t)]  (30)

As such, the uncertainty caused by resistor temperature T_(R)(t) iseliminated.

Under the voltage detection mode, the normalization factor in Equation(18) can be expressed as:

$\begin{matrix}{{Q_{i}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {V_{i}(t)}} \right\rbrack}{\sum\limits_{i = 1}^{N}{\sigma \left\lbrack {\log \; {V_{i}(t)}} \right\rbrack}}} & (31)\end{matrix}$

The mathematic correlation between optical measurement and blood glucoseconcentration can then be established according to Equation (19) orEquation (20), under corresponding calibration conditions.

Similarly, the ratio factor defined by Equation (21) can be expressedas:

$\begin{matrix}{{Y_{ij}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {V_{i}(t)}} \right\rbrack}{\sigma \left\lbrack {\log \; {V_{j}(t)}} \right\rbrack}} & (32)\end{matrix}$

The mathematic correlation between optical measurement and blood glucoseconcentration can then be established according to Equation (22) orEquation (23), under corresponding calibration conditions. The schematiccircuit diagram of a typical preamplifier 400 also includes a feedbackcapacitor 404, an operational amplifier 406, and a ground connection410.

Digitization

The voltage output 412 from the preamplifier 400 is usually digitizedusing an analog-to-digital convertor (ADC). The digitized signal is thensent to a computer for data analysis. The output of ith ADC, incommunication with ith preamplifier that is in coupling with ithphotodiode 408 collecting light power at wavelength A_(z), can beexpressed by the following Equation:

(ADC)_(i)=(ADC)_(0i) +G{└I _(D)(λ_(i) ,t)+I _(Dark,i) ┘R _(i) +A_(0i)}  (33)

In the above Equation (33), (ADC)_(0i) is the offset of ith ADC, G isthe nominal ADC Gain used during the detection, I_(D)(λ_(i),t) is thephotocurrent of ith photodiode detector, I_(Dark,i) is the dark currentof ith photodiode detector, R_(i)=R_(0i)[1+χ_(i)(T_(Ri)(t)−25° C.)]] isthe resistance of feedback resistor of ith preamplifier, and A_(0i) isthe offset of ith preamplifier.

The contribution of the three factors, (ADC)_(0i), I_(Dark,i), andA_(0i) can be removed by carrying out a dark measurement with the lightsource turned off right before or after the corresponding fingermeasurement. When the light source is turned off, the above Equation(33) becomes:

(ADC)_(Dark,i)=(ADC)_(0i) +G _(i)(I _(Dark,i) R _(i) +A _(0i))  (34)

The difference between the two above Equations (33) and (34) reflectsADC output corresponding to the photocurrent:

α(ADC)_(i)=(ADC)_(i)−(ADC)_(Dark,i) =G _(i) I _(D)(λ_(i) ,t)R _(i)  (35)

Applying common logarithm to both side of the Equation (35), oneobtains:

log Δ(ADC)_(i)=log G _(i)+log I _(D)(λ_(i) ,t)+log R _(i)  (36)

G_(i) and R_(i) can be considered as constants as long as the timeinterval between the finger measurement and the dark measurement isshort. As such, one obtains:

σ[log Δ(ADC)_(i)]=σ[log I _(D)(λ_(i) ,t)]  (37)

Substituting Equation (26) into Equation (37), one further obtains:

σ[log Δ(ADC)_(i)]=σ[log P(λ_(i) ,t)]  (38)

Based on Equation (37), the normalization factor defined by Equation(18) can be expressed as:

$\begin{matrix}{{Q_{i}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{i}} \right\rbrack}{\sum\limits_{i = 1}^{N}{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{i}} \right\rbrack}}} & (39)\end{matrix}$

The mathematic correlation between optical measurement and blood glucoseconcentration can then be established according to Equation (19) or(20), under corresponding calibration conditions.

Similar to normalization, the ratio factor defined by Equation (21) canbe expressed as:

$\begin{matrix}{{Y_{ij}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{i}} \right\rbrack}{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{j}} \right\rbrack}} & (40)\end{matrix}$

The correlation between optical measurement and blood glucoseconcentration can then be established according to Equations (22) or(23), under corresponding calibration conditions.

Thus, there has been shown and described several embodiments of a novelinvention. As is evident from the foregoing description, certain aspectsof the present invention are not limited by the particular details ofthe examples illustrated herein, and it is therefore contemplated thatother modifications and applications, or equivalents thereof, will occurto those skilled in the art. The terms “have,” “having,” “includes,”“including,” and similar terms as used in the foregoing specificationare used in the sense of “optional” or “may include” and not as“required.” Many changes, modifications, variations and other uses andapplications of the present construction will, however, become apparentto those skilled in the art after considering the specification and theaccompanying drawings. All such changes, modifications, variations andother uses and applications, which do not depart from the spirit andscope of the invention, are deemed to be covered by the invention, whichis limited only by the claims that follow. It should be understood thatthe embodiments disclosed herein include any and all combinations offeatures described in any of the dependent claims.

1-14. (canceled)
 15. A system for detecting glucose in a biologicalsample, comprising: at least one light beam generating structure; atleast one photocurrent signal generating light detector; and a lightabsorbance change determining algorithm implemented processor programmedto calculate a change in a light absorption caused by blood in abiological sample and configured to receive the output photocurrentsignal from the at least one photocurrent signal generating lightdetector and based on the received output photocurrent signal todetermine a blood glucose level associated with the biological samplepresent in the target area.
 16. The system of claim 15, wherein thelight absorbance change determining algorithm implemented processor isprogrammed to calculate the attenuance attributable to blood in thebiological sample based on the received output photocurrent signal. 17.The system of claim 15, wherein the light absorbance change determiningalgorithm implemented processor is programmed to eliminate effect ofuncertainty caused by temperature dependent detector response of the atleast one photocurrent signal generating light detector by calculatingthe standard deviation of a logarithm of the time dependent voltagesignal.
 18. The system of claim 15, wherein the light absorbance changedetermining algorithm implemented processor is configured to calculate anormalization factor Q_(i)(C,T) based on an output voltage V_(i)(t) ofthe i^(th) preamplifier as a function of time, where σ is standarddeviation according to the equation:${{Q_{i}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {V_{i}(t)}} \right\rbrack}{\sum\limits_{i = 1}^{N}{\sigma \left\lbrack {\log \; {V_{i}(t)}} \right\rbrack}}};$wherein T is a temperature of the biological sample and C is theconcentration of blood glucose in the biological sample.
 19. The systemof claim 15, wherein the light absorbance change determining algorithmimplemented processor is configured to calculate a ratio factorY_(ij)(C,T) based on an output voltage V_(i)(t) of the preamplifier andan output voltage V_(j)(t) of the j^(th) preamplifier as a function oftime, where σ is standard deviation according to the equation:${{Y_{ij}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {V_{i}(t)}} \right\rbrack}{\sigma \left\lbrack {\log \; {V_{j}(t)}} \right\rbrack}};$wherein T is a temperature of the biological sample and C isconcentration of blood glucose in the biological sample.
 20. The systemof claim 15, further comprising an analog-to-digital convertor having adigitized voltage output.
 21. The system of claim 15, wherein the lightabsorbance change determining algorithm implemented processor isconfigured to calculate a normalization factor Q_(i)(C,T) based on avoltage output Δ(ADC)_(i) of an i^(th) analog-to-digital converter,where σ is standard deviation according to the equation:${Q_{i}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{i}} \right\rbrack}{\sum\limits_{i = 1}^{N}{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{i}} \right\rbrack}}$wherein T is the temperature of the biological sample and C is theconcentration of blood glucose in the biological sample.
 22. The systemof claim 15, wherein the light absorbance change determining algorithmimplemented processor is configured to calculate a ratio factorY_(ij)(C,T) based on a voltage output Δ(ADC)_(i) of the i^(th)analog-to-digital convertor and a voltage output Δ(ADC)_(j) of thej^(th) analog-to-digital convertor, where σ is standard deviationaccording to the equation:${Y_{ij}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{i}} \right\rbrack}{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{j}} \right\rbrack}$wherein T is a temperature of the biological sample and C is aconcentration of blood glucose in the biological sample.
 23. The systemof claim 15, wherein the at least one light beam generating structure isconfigured to strike a target area of a biological sample and generatesone or more light beams having a wavelength in a range between 800 nmand 1600 nm.
 24. The system of claim 15, wherein the at least onephotocurrent signal generating light detector includes a preamplifierhaving a feedback resistor, positioned to receive light from the atleast one light source and to generate an output photocurrent signal,having a time dependent current, which is indicative of the power oflight detected.
 25. The system of claim 24, wherein the preamplifier hasa feedback resistor, which is configured to convert the time dependentcurrent into a time dependent voltage signal.
 26. A method for detectingglucose in a biological sample, comprising: utilizing at least one lightbeam generating structure to generate one or more light beams to strikea target area of a biological sample; utilizing at least onephotocurrent signal generating light detector; and receiving an outputphotocurrent signal from the at least one photocurrent signal generatinglight detector with a light absorbance change determining algorithmimplemented processor programmed to calculate a change in a lightabsorption caused by blood in the biological sample and based on thereceived output photocurrent signal to determine a blood glucose levelassociated with the biological sample present in the target area basedon the calculated attenuance with the light absorbance changedetermining algorithm implemented processor.
 27. The method of claim 26,wherein the one or more light beams have a wavelength in a range between800 nm and 1600 nm.
 28. The method of claim 26, further comprising apreamplifier having a feedback resistor, positioned to receive lightfrom the at least one light source and generating an output photocurrentsignal, having a time dependent current, which is indicative of thepower of light detected.
 29. The method of claim 28, further comprisingconverting the time dependent current into a time dependent voltagesignal.
 30. The method of claim 26, further comprising calculatingattenuance attributable to blood in the biological sample present in thetarget area by using the light absorbance change determining algorithmimplemented processor.
 31. The method of claim 26, further comprisingcalculating a normalization factor

_(i)(C,T) based on the voltage output V_(i)(t) of the i^(th)preamplifier as a function of time, where σ is standard deviationaccording to the equation:${{Q_{i}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {V_{i}(t)}} \right\rbrack}{\sum\limits_{i = 1}^{N}{\sigma \left\lbrack {\log \; {V_{i}(t)}} \right\rbrack}}};$wherein T is a temperature of the biological sample and C is aconcentration of blood glucose in the biological sample.
 32. The methodof claim 26, further comprising calculating a ratio factor Y_(ij)(C,T)based on an output voltage V_(i)(t) of the i^(th) preamplifier and anoutput voltage V_(j)(t) of the j^(th) preamplifier as a function oftime, where σ is standard deviation according to the equation:${{Y_{ij}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {V_{i}(t)}} \right\rbrack}{\sigma \left\lbrack {\log \; {V_{j}(t)}} \right\rbrack}};$wherein T is a temperature of the biological sample and C is aconcentration of blood glucose in the biological sample.
 33. The methodof claim 26, further comprising utilizing an analog-to-digital convertorhaving a digitized voltage output.
 34. The method of claim 26, furthercomprising calculating a normalization factor

_(i)(C,T) based on a voltage output Δ(ADC)_(i) of an i^(th)analog-to-digital convertor, where σ is standard deviation according tothe equation:${Q_{i}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{i}} \right\rbrack}{\sum\limits_{i = 1}^{N}{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{i}} \right\rbrack}}$wherein T is a temperature of the biological sample and C is aconcentration of blood glucose in the biological sample.
 35. The methodof claim 26, further comprising calculating a ratio factor Y_(ij)(C,T)based on a voltage output Δ(ADC)_(i) of an i^(th) analog-to-digitalconvertor and a voltage output Δ(ADC)_(j) of a j^(th) analog-to-digitalconvertor, where σ is standard deviation according to the equation:${Y_{ij}\left( {C,T} \right)} = \frac{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{i}} \right\rbrack}{\sigma \left\lbrack {\log \; {\Delta ({ADC})}_{j}} \right\rbrack}$wherein T is a temperature of the biological sample and C is aconcentration of blood glucose in the biological sample.
 36. A systemfor detecting glucose in a biological sample, comprising: at least onelight beam generating structure; at least one photocurrent signalgenerating light detector; and a light absorbance change determiningalgorithm implemented processor programmed to calculate a change in alight absorption caused by blood in a biological sample and configuredto receive the output photocurrent signal from the at least onephotocurrent signal generating light detector and based on the receivedoutput photocurrent signal to determine a blood glucose level associatedwith the biological sample present in the target area, wherein the lightabsorbance change determining algorithm implemented processor isprogrammed to eliminate effect of uncertainty caused by temperaturedependent detector response of the at least one photocurrent signalgenerating light detector.